Because I play pick up games and fantasy sports I often wonder…how would league/tournament look like if NBA players were distributed among teams by different metrics?

You can approach this topic in many ways but metrics agree about value of top stars so I don’t think standard draft of all players could work without issues typical to snake drafts in fantasy NBA.
Instead I wanted to focus and magnify differences in evaluation and here’s a twisted idea:
based only on last season’s numbers I subtracted player’s ranking according to given metric from average ranking for him based on 4 other statistics and sorted it by the biggest difference for each metric. In other words, I wanted to find players for each statistic which were valued the most by it when compared to others.

To make it clear, let me give you an example, last season Andrea Bargnani was ranked by PER as 80th best player in the league. Win Shares, Wins Produces, RAPM and ASPM graded him below Top200 [to be exact 216th, 245th, 231st, 209th for an average of 225.25].
So PER viewed Bargnani’s 2010-11 season 145.25 slots higher than the competition, and it was the highest difference for it, so he would be selected for the tournament as a representant for PER ;-) Got it?

The rest of such draft would look like this… [I included 9 top players for each metric with difference in parenthesis and extra players were mentioned because of positional need]Read the rest of this entry »

Again based on great database prepared by Alex at sportskeptic.wordpress.com and for the last 5 years I ranked each player [with at least 1000 minutes played] according to five different metrics [PER, Win Shares per 48 minutes, Wins Produced per 48, new RAPM and ASPM] and then I calculated standard deviation of each player’s ranking and below you will see only those with the lowest number each year meaning the differences in evaluation between statistics were the smallest.

So I’ve collected Yahoo’s split statistics for 953 player-seasons [those who played at least 41 total games in the last three years] and divided them into 5 groups based on a different situation:
“Total for a season”, “0 Days Rest”, “1 Day Rest”, “2 Days Rest”, and “3+ Days Rest”.

Here are basic averages of those groups:
[where WS/36 = Win Score per 36 minutes and GS/36 = Game Score per 36. Also please note that shooting percentages where calculated on a league level so FG% = “sum of all avg fgm” / “sum of all avg fga”]:

Situation

GP

Min/g

FG%

3P%

FT%

Reb/g

As/g

Stl/g

TO/g

BL/g

Fls/g

Pts/g

WS/36

GS/36

Season

68,7

24,73

46,04

36,19

76,79

4,22

2,20

0,75

1,39

0,49

2,11

10,33

6,32

10,81

0 daysof rest

15,7

25,18

45,86

36,58

76,91

4,24

2,17

0,73

1,40

0,49

2,17

10,43

6,11

10,58

1 dayof rest

34,0

25,12

46,02

35,94

76,88

4,30

2,25

0,78

1,41

0,50

2,13

10,47

6,37

10,85

2 daysof rest

10,8

24,67

46,37

36,54

76,81

4,21

2,23

0,75

1,39

0,49

2,10

10,38

6,34

10,85

3 daysof rest

8,2

23,01

46,08

36,26

75,68

3,99

2,08

0,73

1,32

0,49

2,02

9,73

6,31

10,82

4-5% loss maybe isn’t much of a difference but back-to-backs are indeed the worst and 1-to-2 days of rest seems the best time to play. On average that’s because of fouls, steals, rebounds, blocks, assists and FG%.